Teaching

I have served as a Teaching Assistant at UNIST across multiple semesters in the Department of Computer Science and Engineering, supporting both an undergraduate AI programming course and a foundational discrete-math course.

Teaching philosophy

My approach to teaching is rooted in the same principle that guides my research: mastery comes from working through problems, not from passively watching solutions. I aim to make recitations and office hours a low-pressure space where students can think aloud, make mistakes, and build intuition. In AI/ML courses I emphasize connecting code to mathematics — translating linear algebra and probability into NumPy / PyTorch, then back to a clear conceptual picture of what a model is doing. In foundational courses such as discrete mathematics, I focus on helping students develop proof-writing fluency by modeling the reasoning steps explicitly and then withdrawing scaffolding as they grow more confident.

Courses

Mentorship & service

• Mentoring junior lab members at the Vision & Learning Lab on 3D human reconstruction, Gaussian splatting, and continual-learning projects.
• Reviewing internal lab manuscripts and providing feedback on figures, experiment design, and writing.